BUSINESS QUESTION: Which are the top 5 products that are going to be more profitable for the company?

What data do we have?

New product attributes and existing product attributes.

• Predicting sales of four different product types: PC, Laptops, Netbooks and Smartphones
• Assessing the impact services reviews and customer reviews have on sales of different product types

1. Data cleaning

2. Data exploration

3. Pre-process: feature selection (correlation matrix) & feature engineering

4. Modalization: linear regresion, KNN, SVM, Random forest, GBM

5. Error analysis

Transformation to factor:

fact_var <- c("ProductType","ProductNum")
ex_prod[,fact_var] <- apply(ex_prod[,fact_var], 2, as.factor)

Giving names to the rows:

ex_prod <- tibble::column_to_rownames(.data = ex_prod,
                                     var = "ProductNum")
ex_prod$ProductNum <- NULL

# train and test
train_id <- createDataPartition(y = ex_prod$Volume, p = 0.75, list = F)
train <- ex_prod[train_id,]
test <- ex_prod[-train_id,]

# create linear regression model
mod_lm <- lm(formula = Volume ~ ., data = train)

# model performance
postResample(pred = predict(object = mod_lm, newdata = test),
             obs = test$Volume)

##         RMSE     Rsquared          MAE 
## 1.394552e-12 1.000000e+00 6.345334e-13

Main predictors:

1. x5StarReviews
2. ProductTypeGameConsole